The main focus of my thesis is on making machine learning algorithms more energy efficient. In particular, I have studied the energy consumption patterns of streaming algorithms, and then proposed new algorithm extensions that reduce their energy consumption. Some of my research interests are the following: Energy Efficiency in Machine Learning, Green AI, High Performance Computing, Big Data and Streaming Data, Green Computing.

News

July’19: Our paper “Estimation of Energy Consumption in Machine Learning” has been accepted at the Journal of Parallel and Distributed Computing (JPDC). Soon arXiv version online!

June’19: Gave a talk on Sustainable Machine Learning at the Women in Tech event “Women Influencers in Disruptive Technologies”, in Gothenburg, Sweden.

April’19: From August 15 I will be starting a new position as a Data Scientist at Ekkono Solutions.

Organizing / PC / other

We are currently organizing the second edition of the Green Data Mining workshop: Second International Workshop on Energy Efficient Scalable Data Mining and Machine Learning, held in conjunction with ECML-PKDD 2019, in September in Würzburg, Germany. Green Data Mining

I organized the 1st International Workshop on Energy Efficient Data Mining and Knowledge Discovery, held in conjunction with ECML-PKDD 2018, in September in Dublin, Ireland. Green Data Mining

I was helping at organizing the WiML dinner held at ICML, in Stockholm, July 2018.